ENRICHING DRIVING EXPERIENCE WITH CLOUD ASSISTANCE

• US 20140278047A1
• Filed: 03/14/2013
• Published: 09/18/2014
• Est. Priority Date: 03/14/2013
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Bahl, Paramvir, Kandula, Srikanth, Iyer, Anand Padmanabha
• Granted Patent: US 9,092,984 B2
• Time in Patent Office: Days
• US Class Current: 701/301
• CPC Class Codes: G08G 1/161 Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/164 Centralised systems, e.g. external to vehicles
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

Described is a technology by which driver safety technology such as collision detection is implemented via mobile device (e.g., smartphone) sensors and a cloud service that processes data received from vehicles associated with the devices. Trajectory-related data is received at the cloud service and used to predict collisions between vehicles and/or lane departures of vehicles. To operate the service in real-time with low latency, also described is dividing driving areas into grids, e.g., based upon traffic density, having parallel grid servers each responsible for only vehicles in or approaching its own grid, and other parallel/distributed mechanisms of the cloud service.

Claims

1. In a computing environment, a method performed at least in part on at least one processor comprising:

receiving, at a service, a wireless communication that is sent from a mobile device associated with a vehicle, the wireless communication comprising information corresponding to a trajectory of the vehicle;

determining from the information whether the vehicle is at risk of a collision, and if so, sending alert-related data to the vehicle.

2. The method of claim 1 wherein determining from the information whether the vehicle is at risk of a collision comprises computing, based upon the information and other received information, whether the vehicle is too close to another vehicle.

3. The method of claim 1 wherein determining from the information whether the vehicle is at risk of a collision computing based upon the information whether the vehicle is in a state of lane departure.

4. The method of claim 1 wherein the information comprises coordinates and speed-related data obtained via sensor data of the mobile device, and further comprising computing the trajectory of the vehicle at the service based at least in part on the coordinates and speed-related data.

5. The method of claim 4 wherein computing the trajectory of the vehicle further comprises, using at least one of:

course data, acceleration data, rotation data or yaw data.

6. The method of claim 4 wherein computing the trajectory of the vehicle further comprises, using at least one of:

route history data, traffic estimate data, road segment data or roadway data.

7. The method of claim 1 further comprising dividing a set of locations into grids, and determining from the information at least one grid, each grid corresponding to a location in which the vehicle is in or is projected to possibly be in before updated information from the mobile device is received

8. The method of claim 1 wherein dividing the set of locations into grids comprises determining areas of the grids based at least in part on vehicle density within the areas.

9. The method of claim 1 further comprising, receiving an identifier of the mobile device at a front end server of the service, hashing the identifier to determine a back end server associated with the mobile device, and at the back end server, determining from the information at least one grid server, each grid server corresponding to a location in which the vehicle is in or is projected to possibly be in before updated information from the mobile device is received.

10. The method of claim 1 further comprising, receiving a request from the mobile device at the service, and responding to the request with data obtained at the cloud service, including data related to at least one of generating a map, generating traffic information, or traffic planning.

11. In a computing environment, a system comprising, a cloud service configured with servers, including a plurality of grid servers, each grid server associated with a grid of plurality of grids, each grid corresponding to an area, each grid server configured to compute whether vehicles that are known to the server to be in or approaching its associated grid are at risk of collision, and if so, to output alert-related data for communication to at least one of the vehicles that is at risk of collision.

12. The system of claim 11 wherein the cloud service is further configured to raise an alert to a recipient, including a recipient not in a vehicle, or a recipient in a vehicle based upon a distance of the vehicle to a location.

13. The system of claim 12 wherein the mobile devices comprise smartphones or devices built into the vehicles, or a combination of at least one smartphone and at least one device built into a vehicle.

14. The system of claim 11 wherein at least one gird server comprises a spatial store and a query engine that share information in a common memory.

15. The system of claim 11 wherein the service includes a master server configured to determine each grid'"'"'s coverage area, and associate a grid server with each grid.

16. The system of claim 11 wherein the cloud service is configured to receive sensed data from a mobile devices of a vehicle, and wherein the cloud service comprise at least one server that is configured to compensate for inaccuracies in the sensed data, including by combining the sensed data with at least one of:

information from one or more other sensors, information from one or more other vehicles and historical information associated with the vehicle or a mobile device.

17. The system of claim 11 wherein the cloud service is configured to output alert-related data for communication to a plurality of vehicles, in which the vehicles are determined based upon at least one of:

velocity, distance, location estimation error, cloud service latency, or server computing delay.

18. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising:

receiving trajectory-related data from a vehicle mobile device;

determining from the trajectory-related data at least one grid corresponding to the vehicle mobile device; and

querying based upon the trajectory-related data of the vehicle whether the vehicle is at risk of a collision within the grid, and if so, outputting alert-related data.

19. The one or more computer-readable media of claim 18 wherein querying based upon the trajectory-related data comprises determining from the trajectory-related data of the vehicle and other trajectory-related data of at least one other vehicle whether two or more vehicles are within a threshold distance of one another, and if so, outputting the alert-related data.

20. The one or more computer-readable media of claim 18 wherein querying based upon the trajectory-related data comprises determining from the trajectory-related data of the vehicle and road-related information whether the vehicle is in a lane departure state.